Electrical scooter alert

ABSTRACT

There may be provided a method for electrical scooter alert, the method may include sensing sensed information about an environment of a vehicle; detecting, based on the sensed information, a situation related to the environment; detecting, based on the sensed information, a electrical scooter within the environment; predicting, using a machine leaning process and based in the situation, a future behavior of the electrical scooter and an impact of the future behavior of the electrical scooter on a future progress of the vehicle; and responding to the predicting.

BACKGROUND

driving with extra care. There is a growing need to autonomously adapt the driving near school sones.

SUMMARY

A method, system and non-transitory computer readable medium for electrical scooter alert.

BRIEF DESCRIPTION OF THE DRAWINGS

The embodiments of the disclosure will be understood and appreciated more fully from the following detailed description, taken in conjunction with the drawings in which:

FIG. 1A illustrates an example of a method;

FIG. 1B illustrates an example of a signature;

FIG. 1C illustrates an example of a dimension expansion process;

FIG. 1D illustrates an example of a merge operation;

FIG. 1E illustrates an example of hybrid process;

FIG. 1F illustrates an example of a method;

FIG. 1G illustrates an example of a method;

FIG. 1H illustrates an example of a method;

FIG. 1I illustrates an example of a method;

FIG. 1J illustrates an example of a method;

FIG. 1K illustrates an example of a method;

FIG. 1L illustrates an example of a method;

FIG. 1M illustrates an example of a system;

FIG. 1N is a partly-pictorial, partly-block diagram illustration of an exemplary obstacle detection and mapping system, constructed and operative in accordance with embodiments described herein;

FIG. 1O illustrates an example of a method;

FIG. 1P illustrates an example of a method;

FIG. 1Q illustrates an exampled of a method;

FIG. 2 illustrates an example of a method;

FIG. 3 illustrates an example of a method; and

FIG. 4 is an example of a situation.

DESCRIPTION OF EXAMPLE EMBODIMENTS

The specification and/or drawings may refer to an image. An image is an example of a media unit. Any reference to an image may be applied mutatis mutandis to a media unit. A media unit may be an example of sensed information. Any reference to a media unit may be applied mutatis mutandis to a natural signal such as but not limited to signal generated by nature, signal representing human behavior, signal representing operations related to the stock market, a medical signal, and the like. Any reference to a media unit may be applied mutatis mutandis to sensed information. The sensed information may be sensed by any type of sensors—such as a visual light camera, or a sensor that may sense infrared, radar imagery, ultrasound, electro-optics, radiography, LIDAR (light detection and ranging), etc.

The specification and/or drawings may refer to a processor. The processor may be a processing circuitry. The processing circuitry may be implemented as a central processing unit (CPU), and/or one or more other integrated circuits such as application-specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), full-custom integrated circuits, etc., or a combination of such integrated circuits.

Any combination of any steps of any method illustrated in the specification and/or drawings may be provided.

Any combination of any subject matter of any of claims may be provided.

Any combinations of systems, units, components, processors, sensors, illustrated in the specification and/or drawings may be provided.

There may be provided a system, method and computer readable medium for child forward collision warning.

The child forward collision warning may be responsive to a situation.

Various examples of a situation may be at least one of (a) a location of the vehicle, (b) one or more weather conditions, (c) one or more contextual parameters, (d) a road condition, (e) a traffic parameter.

Various examples of a road condition may include the roughness of the road, the maintenance level of the road, presence of potholes or other related road obstacles, whether the road is slippery, covered with snow or other particles.

Various examples of a traffic parameter and the one or more contextual parameters may include time (hour, day, period or year, certain hours at certain days, and the like), a traffic load, a distribution of vehicles on the road, the behavior of one or more vehicles (aggressive, calm, predictable, unpredictable, and the like), the presence of pedestrians near the road, the presence of pedestrians near the vehicle, the presence of pedestrians away from the vehicle, the behavior of the pedestrians (aggressive, calm, predictable, unpredictable, and the like), risk associated with driving within a vicinity of the vehicle, complexity associated with driving within of the vehicle, the presence (near the vehicle) of at least one out of a kindergarten, a school, a gathering of people, and the like. A contextual parameter may be related to the context of the sensed information—context may be depending on or relating to the circumstances that form the setting for an event, statement, or idea.

There may be provide a system, method and a non-transitory computer readable medium that stores instruction for generating a electrical scooter alert.

The method may include identifying electrical scooter in a semi supervised manner. The first phase may be semi-supervised and may include collect sensed information from one or more sensors (video, audio, radar, lidar, etc) of mounted electrical scooters in various angles. The second phase may be unsupervised and may include collecting untagged samples of driving. Match tagged samples to untagged samples to generate large clusters of scooters. Examples of situations that could be clusters: sidewalks, junction, traffic light, free/little/congested traffic, highway/urban/rural road, night, day, rain, sunny, etc.

The method may proceed by building a behavioral model of each of scooter agent (agent being represented by a cluster). Once a scooter is identified—tracking it during its drive (for multiple samples). Building a behavioral model by clustering different types of situations and the corresponding scooters' behaviors. A situation may be associated with a collection of possible behaviors.

For example—situation—stopping before a red light. Possible behaviors may include (a) stopping after the vehicle ahead and waiting for the traffic light to turn green, (b) maneuvering and passing through the free spaces between the stopping vehicles and stopping near or after the stop line, and (c) tunning a red light or treating it as a stop sign.

Another example—situation—busy highway/traffic jam. Possible behaviors may include (a) maneuvering at the free spaces while bypassing vehicles from right and left, (b) driving next to the road margins.

A further example—situation—urban area driving. Possible behaviors may include (a) driving on sidewalks, and (b) not obeying traffic rules such as stop sign, right of way.

An example of situation detection is illustrated in US patent application 20200130708 which is incorporated herein by reference.

The method may also include a prediction phase of predicting the behavior of a electrical scooter given the situation.

The prediction can be generated by a process that may include, for example, making statistical inferences from all samples and use unsupervised machine learning to automatically build a predicted probabilistic trajectory according to the behavioral model that is able to detect a specific electrical scooter and scene and predict the probability of each behavior and as a result its trajectory.

For example—predicting bypass direction or cut in and calculate their time to collision (TTC). TTC may be calculated based on the ego vehicle velocity, and acceleration (can be provided over a telemetric bus such as the Canbus). While using the lateral distance estimation to the objects, calculated based on the object's ground intersection.

FIG. 2 illustrates method 100 for electrical scooter alert.

Method 100 may start by step 110 of sensing sensed information about an environment of a vehicle. Any sensors can be used.

Step 110 may be followed by step 120 of detecting, based on the sensed information, a situation related to the environment. An example of detecting situations is illustrated in US patent application 20200130708 which is incorporated herein by reference.

Step 110 may also be followed by step 130 of detecting, based on the sensed information, an electrical scooter within the environment. Any sensor may be used.

Step 130 may include detecting based on electrical scooter identifiers. Such electrical scooter identifiers may be generated during 200. Method 200 may be executed before the execution of method 100 and its outcomes may be used in the execution of method 100.

Steps 120 and 130 may be followed by step 140 of predicting, using a machine leaning process and based in the situation, a future behavior of the electrical scooter and an impact of the future behavior of the electrical scooter on a future progress of the vehicle.

The machine learning process may be trained during method 200 or may be configured to represent the situation behavioral information gained during method 200.

The prediction of the future behavior of the electrical scooter may be determined based on the situation behavioral information gained in method 200.

The future progress of the vehicle may be initially determined by the vehicle current location, speed, direction of movement and any other kinetic parameters of the vehicle, as well as the environment (current lane on which the vehicle is driving—size, shape, inclination, curvature, direction, any other lanes and or roads in the vicinity of the vehicle, traffic signs, traffic lights, and any other environmental parameter related to the progress of the vehicle—including weather condition), whether the vehicle is destined or scheduled to follow a certain path.

The impact of the electrical scooter one the vehicle can be predicted by the spatial and timing relationship between the predicted progress of the vehicle and one or more predicted future behavior of the electrical scooter.

Step 140 may include calculating the spatial and timing and relationship between the predicted progress of the vehicle and each potential relevant future behavior of the electrical scooter. Each potential relevant future behavior of the electrical scooter (a) has probability of occurrence that exceeds a threshold, and (b) impacts the future progress of the vehicle when occurs.

The probability of occurrence of a potential relevant future behavior at certain situation may be determined in method 200. The threshold may be determined in any manner—for example may be 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30 percent, and the like. The threshold may be determined based on the occurrence of such a behavior in sensed information units obtained during one or more monitoring periods (see method 200).

Step 140 may be followed by step 150 of responding to the predicting.

Step 150 may be responsive to a probability distribution of potential future behaviors of the electrical scooter. Thus—if there are multiple possible future behaviors, each having its probability—then the responding may be responsive to all behaviors that may impact the vehicle and of a probability that exceeds a threshold, yet for another example—step 150 may ignore any possible behavior that is not expected to affect the vehicle and take into account only those who are expected to impact the vehicle—having any probability included in the probability distribution. On the other hand step 150 may respond to a possible future behavior based on any trade-off between behavior probability and potential impact on the vehicle. For example—may be responsive to the mean or expected value—risk multiplied by the amount of risk.

Step 150 may be responsive to an existence of at least one potential relevant future behavior of the electrical scooter.

Step 150 may include at least one out of the following: generating a human perceivable information about the predicting, requesting an autonomous module to drive the vehicle based on the predicting; proposing a route of the vehicle; storing an outcome of the predicting; activating a driving mode in response to the outcome of the predicting; activating safety measures in response to the outcome of the predicting; and generating a human perceivable alert outside the vehicle.

FIG. 4 illustrates a situation in which a vehicle 11 drives along lane 22 towards a junction 24 that is preceded by a STOP sign 23. The lane is clear until the STOP sign.

The vehicle senses an electrical scooter 22 at the left lane 22. Two vehicles 12 and 13 are located at left lane 22 and both stopped before the stop sign. The electrical scooter is a certain class that has three potential relevant future behaviors—a first potential relevant future behavior 31 includes maintaining in the left lane and standing after the two vehicles 12 and 13, a second potential relevant future behavior 32 includes deviating to lane 21 and temporarily stopping before the STOP sign at the center (or about the center) of lane 21, and a third potential relevant future behavior 33 is driving between lane 21 and 22 and positioning himself between lanes 21 and 22.

The first potential relevant future behavior has a first probability of occurrence P1, the second potential relevant future behavior has a second probability of occurrence P2, the third potential relevant future behavior has a third probability of occurrence P3.

The first potential relevant future behavior 31 does not affect vehicle 11, the third potential relevant future behavior 33 may slightly affect vehicle, and the second potential relevant future behavior 32 affects the vehicle 11 that will have to space itself apart from the electrical scooter.

The responding may take into account one or more of the first till third potential relevant future behaviors, and their potential affect on vehicle 11. The responding may also take into account P1, P2 and P3. For example—it may take the worst case scenario (second potential relevant future behavior 32) and suggest an action that will including introducing space between vehicle and the second potential relevant future behavior 32.

Alternatively—because at least one of the potential relevant future behaviors may affect vehicle 11 and at least one of the potential relevant future behaviors does not affect vehicle 11—vehicle may be required or suggested to slow down, continue monitoring electric scooter until verifying the actual driving pattern.

FIG. 3 illustrates method 200.

Method 200 may start by step 210 of generating electrical scooter identifiers based on a first set of tagged sensed information units and a second set of untagged sensed information units.

Step 210 may start by generating first electrical scooter identifiers based on the first set of tagged sensed information units and the using the first electrical scooter identifiers to detect electrical scooter within the second set of untagged sensed information units.

Step 210 may be followed by step 220 of monitoring behaviors of electrical scooters at multiple situations to provide monitoring results, wherein the monitoring include detecting one or more electrical scooter identifiers within sensed information units obtained during one or more monitoring periods.

The one or more monitoring periods may be of any length the sensed information units and may be obtained by any sensor.

A behavior of an electrical scooter may be represented by the location of the electrical scooter over time—which can be determined by detecting the electrical scooter and monitoring his location. An example of monitoring after a road user and even an example of a compact representation of movement information is illustrated in US patent application publication number 2020/0134327 which is incorporated herein by reference.

Step 220 may be followed by step 230 of generating, based on the monitoring results, situation behavioral information indicative of behaviors of electrical scooters at different situations.

The situation behavioral information may be of any format—model, multiple models, look up tables, mathematical functions, and the like. The multiple models may include a model per a combination of an electrical scooter class and a situation.

The situation behavioral information may be embedded in and/or represented by a trained machine learning process. This trained machine learning process (or at least c duplicate or a similar machine learning process) may be utilized in method 100.

Method 200 may also include step 215 of classifying the electrical scooters to classes. In this case step 230 may include generating situation behavioral information indicative of behaviors of electrical scooters of different classes at different situations.

The classes may be generated in any manner. For example—step 210 may include generating signatures of the electrical scooters. Step 215 may include clustering the signatures of electrical scooters to generate electrical scooter classes.

Step 230 may include determining the severity or risk imposed by each possible electrical scooter behavior. This may involve, for example determining the affect that the behavior of the electrical scooter had on the environment and/or on any road user and/or on the vehicle (acquiring vehicle) that acquired a video that captures the behavior of the electrical scooter—for example whether the acquiring vehicle had to stop, change its path (may also be responsive to the speed or rate of path change), the aggressiveness of the response of the acquiring vehicle, whether the driver (and/or any other passenger) of the acquiring vehicle was stressed (can be measured by biometric sensors) and/or otherwise responded to the behavior of the electrical scooter, whether an accident occurred, estimating one or more distances (for example minimal distance, average distance) between the acquiring vehicle and the electrical scooter during the behavior of the electrical scooter, whether any sensor or system of the vehicle generated an alert, and the like. The affect can be learnt from various sensors such as accelerators, speed meters, telemetric information, processing the visual information acquired by one or more camera, and the like.

An example of generating signatures and clusters are illustrated in US patent application 2020/0130708 which is incorporated herein by reference. An example of modeling a behavior of a road user is illustrated in US patent application 2020/0310458 which is incorporated herein by reference.

Method 200 may process vast amount of data (combination of first set of tagged sensed information units, second set of untagged sensed information units, within sensed information units obtained during one or more monitoring periods, and the like)—and thus provide a highly accurate situation behavioral information indicative of behaviors of electrical scooters at different situations—based on that vast amount of data.

Method 100 may be executed based on the fruits of method 200 and may be dedicated to electrical scooters—thus may be very accurate and very fast (provide a prediction within a fraction of a second (for example less than 0.1 second—much faster that a human)—and provide sufficient time to respond to the presence of an electrical scooter. By concentrating on detecting electrical scooter—the process may be very efficient from memory and computerized resources.

Method 200 may be executed by a computerized system of a vehicle, by a computerized systems of multiple vehicles (for example a by collaboration, by load balancing, by allocating different tasks between the different vehicles, and the like), by one or more computerized systems that does not belong to a vehicle, and the like.

Various example of generating signatures, processing sensed information and clustering are provided below.

Low Power Generation of Signatures

The analysis of content of a media unit may be executed by generating a signature of the media unit and by comparing the signature to reference signatures. The reference signatures may be arranged in one or more concept structures or may be arranged in any other manner. The signatures may be used for object detection or for any other use.

The signature may be generated by creating a multidimensional representation of the media unit. The multidimensional representation of the media unit may have a very large number of dimensions. The high number of dimensions may guarantee that the multidimensional representation of different media units that include different objects is sparse—and that object identifiers of different objects are distant from each other—thus improving the robustness of the signatures.

The generation of the signature is executed in an iterative manner that includes multiple iterations, each iteration may include an expansion operations that is followed by a merge operation. The expansion operation of an iteration is performed by spanning elements of that iteration. By determining, per iteration, which spanning elements (of that iteration) are relevant—and reducing the power consumption of irrelevant spanning elements—a significant amount of power may be saved.

In many cases, most of the spanning elements of an iteration are irrelevant—thus after determining (by the spanning elements) their relevancy—the spanning elements that are deemed to be irrelevant may be shut down a/or enter an idle mode.

FIG. 1A illustrates a method 5000 for generating a signature of a media unit.

Method 5000 may start by step 5010 of receiving or generating sensed information.

The sensed information may be a media unit of multiple objects.

Step 5010 may be followed by processing the media unit by performing multiple iterations, wherein at least some of the multiple iterations comprises applying, by spanning elements of the iteration, dimension expansion process that are followed by a merge operation.

The processing may include:

Step 5020 of performing a k'th iteration expansion process (k may be a variable that is used to track the number of iterations).

Step 5030 of performing a k'th iteration merge process.

Step 5040 of changing the value of k.

Step 5050 of checking if all required iterations were done—if so proceeding to step 5060 of completing the generation of the signature. Else—jumping to step 5020.

The output of step 5020 is a k'th iteration expansion results 5120.

The output of step 5030 is a k'th iteration merge results 5130.

For each iteration (except the first iteration)—the merge result of the previous iteration is an input to the current iteration expansion process.

At least some of the K iterations involve selectively reducing the power consumption of some spanning elements (during step 5020) that are deemed to be irrelevant.

FIG. 1B is an example of an image signature 6027 of a media unit that is an image 6000 and of an outcome 6013 of the last (K'th) iteration.

The image 6001 is virtually segments to segments 6000(i,k). The segments may be of the same shape and size but this is not necessarily so.

Outcome 6013 may be a tensor that includes a vector of values per each segment of the media unit. One or more objects may appear in a certain segment. For each object—an object identifier (of the signature) points to locations of significant values, within a certain vector associated with the certain segment.

For example—a top left segment (6001(1,1)) of the image may be represented in the outcome 6013 by a vector V(1,1) 6017(1,1) that has multiple values. The number of values per vector may exceed 100, 200, 500, 1000, and the like.

The significant values (for example—more than 10, 20, 30, 40 values, and/or more than 0.1%, 0.2%. 0.5%, 1%, 5% of all values of the vector and the like) may be selected. The significant values may have the values—but may be selected in any other manner.

FIG. 1B illustrates a set of significant responses 6015(1,1) of vector V(1,1) 6017(1,1). The set includes five significant values (such as first significant value SV1(1,1) 6013(1,1,1), second significant value SV2(1,1), third significant value SV3(1,1), fourth significant value SV4(1,1), and fifth significant value SV5(1,1) 6013(1,1,5).

The image signature 6027 includes five indexes for the retrieval of the five significant values—first till fifth identifiers ID1-ID5 are indexes for retrieving the first till fifth significant values.

FIG. 1C illustrates a k'th iteration expansion process.

The k'th iteration expansion process start by receiving the merge results 5060′ of a previous iteration.

The merge results of a previous iteration may include values are indicative of previous expansion processes—for example—may include values that are indicative of relevant spanning elements from a previous expansion operation, values indicative of relevant regions of interest in a multidimensional representation of the merge results of a previous iteration.

The merge results (of the previous iteration) are fed to spanning elements such as spanning elements 5061(1)-5061(J).

Each spanning element is associated with a unique set of values. The set may include one or more values. The spanning elements apply different functions that may be orthogonal to each other. Using non-orthogonal functions may increase the number of spanning elements—but this increment may be tolerable.

The spanning elements may apply functions that are decorrelated to each other—even if not orthogonal to each other.

The spanning elements may be associated with different combinations of object identifiers that may “cover” multiple possible media units. Candidates for combinations of object identifiers may be selected in various manners—for example based on their occurrence in various images (such as test images) randomly, pseudo randomly, according to some rules and the like. Out of these candidates the combinations may be selected to be decorrelated, to cover said multiple possible media units and/or in a manner that certain objects are mapped to the same spanning elements.

Each spanning element compares the values of the merge results to the unique set (associated with the spanning element) and if there is a match—then the spanning element is deemed to be relevant. If so—the spanning element completes the expansion operation.

If there is no match—the spanning element is deemed to be irrelevant and enters a low power mode. The low power mode may also be referred to as an idle mode, a standby mode, and the like. The low power mode is termed low power because the power consumption of an irrelevant spanning element is lower than the power consumption of a relevant spanning element.

In FIG. 1C various spanning elements are relevant (5061(1)-5061(3)) and one spanning element is irrelevant (5061(J)).

Each relevant spanning element may perform a spanning operation that includes assigning an output value that is indicative of an identity of the relevant spanning elements of the iteration. The output value may also be indicative of identities of previous relevant spanning elements (from previous iterations).

For example—assuming that spanning element number fifty is relevant and is associated with a unique set of values of eight and four—then the output value may reflect the numbers fifty, four and eight—for example one thousand multiplied by (fifty+forty) plus forty. Any other mapping function may be applied.

FIG. 1C also illustrates the steps executed by each spanning element:

Checking if the merge results are relevant to the spanning element (step 5091).

If—so—completing the spanning operation (step 5093).

If not—entering an idle state (step 5092).

FIG. 1D is an example of various merge operations.

A merge operation may include finding regions of interest. The regions of interest are regions within a multidimensional representation of the sensed information. A region of interest may exhibit a more significant response (for example a stronger, higher intensity response).

The merge operation (executed during a k'th iteration merge operation) may include at least one of the following:

Step 5031 of searching for overlaps between regions of interest (of the k'th iteration expansion operation results) and define regions of interest that are related to the overlaps.

Step 5032 of determining to drop one or more region of interest, and dropping according to the determination.

Step 5033 of searching for relationships between regions of interest (of the k'th iteration expansion operation results) and define regions of interest that are related to the relationship.

Step 5034 of searching for proximate regions of interest (of the k'th iteration expansion operation results) and define regions of interest that are related to the proximity Proximate may be a distance that is a certain fraction (for example less than 1%) of the multi-dimensional space, may be a certain fraction of at least one of the regions of interest that are tested for proximity.

Step 5035 of searching for relationships between regions of interest (of the k'th iteration expansion operation results) and define regions of interest that are related to the relationship.

Step 5036 of merging and/or dropping k'th iteration regions of interest based on shape information related to shape of the k'th iteration regions of interest.

The same merge operations may applied in different iterations.

Alternatively, different merge operations may be executed during different iterations.

FIG. 1E illustrates an example of a hybrid process and an input image 6001.

The hybrid process is hybrid in the sense that some expansion and merge operations are executed by a convolutional neural network (CNN) and some expansion and merge operations (denoted additional iterations of expansion and merge) are not executed by the CNN— but rather by a process that may include determining a relevancy of spanning elements and entering irrelevant spanning elements to a low power mode.

In FIG. 1E one or more initial iterations are executed by first and second CNN layers 6010(1) and 6010(2) that apply first and second functions 6015(1) and 6015(2).

The output of these layers provided information about image properties. The image properties may not amount to object detection. Image properties may include location of edges, properties of curves, and the like.

The CNN may include additional layers (for example third till N'th layer 6010(N)) that may provide a CNN output 6018 that may include object detection information. It should be noted that the additional layers may not be included.

It should be noted that executing the entire signature generation process by a hardware CNN of fixed connectivity may have a higher power consumption—as the CNN will not be able to reduce the power consumption of irrelevant nodes.

FIG. 1F illustrates a method 7000 for low-power calculation of a signature.

Method 7000 starts by step 7010 of receiving or generating a media unit of multiple objects.

Step 7010 may be followed by step 7012 of processing the media unit by performing multiple iterations, wherein at least some of the multiple iterations comprises applying, by spanning elements of the iteration, dimension expansion process that are followed by a merge operation.

The applying of the dimension expansion process of an iteration may include (a) determining a relevancy of the spanning elements of the iteration; and (b) completing the dimension expansion process by relevant spanning elements of the iteration and reducing a power consumption of irrelevant spanning elements until, at least, a completion of the applying of the dimension expansion process.

The identifiers may be retrieval information for retrieving the significant portions.

The at least some of the multiple iterations may be a majority of the multiple iterations.

The output of the multiple iteration may include multiple property attributes for each segment out of multiple segments of the media unit; and wherein the significant portions of an output of the multiple iterations may include more impactful property attributes.

The first iteration of the multiple iteration may include applying the dimension expansion process by applying different filters on the media unit.

The at least some of the multiple iteration exclude at least a first iteration of the multiple iterations. See, for example, FIG. 1E.

The determining the relevancy of the spanning elements of the iteration may be based on at least some identities of relevant spanning elements of at least one previous iteration.

The determining the relevancy of the spanning elements of the iteration may be based on at least some identities of relevant spanning elements of at least one previous iteration that preceded the iteration.

The determining the relevancy of the spanning elements of the iteration may be based on properties of the media unit.

The determining the relevancy of the spanning elements of the iteration may be performed by the spanning elements of the iteration.

Method 7000 may include a neural network processing operation that may be executed by one or more layers of a neural network and does not belong to the at least some of the multiple iterations. See, for example, FIG. 1E.

The at least one iteration may be executed without reducing power consumption of irrelevant neurons of the one or more layers.

The one or more layers may output information about properties of the media unit, wherein the information differs from a recognition of the multiple objects.

The applying, by spanning elements of an iteration that differs from the first iteration, the dimension expansion process may include assigning output values that may be indicative of an identity of the relevant spanning elements of the iteration. See, for example, FIG. 1C.

The applying, by spanning elements of an iteration that differs from the first iteration, the dimension expansion process may include assigning output values that may be indicative a history of dimension expansion processes until the iteration that differs from the first iteration.

The each spanning element may be associated with a subset of reference identifiers. The determining of the relevancy of each spanning elements of the iteration may be based a relationship between the subset of the reference identifiers of the spanning element and an output of a last merge operation before the iteration.

The output of a dimension expansion process of an iteration may be a multidimensional representation of the media unit that may include media unit regions of interest that may be associated with one or more expansion processes that generated the regions of interest.

The merge operation of the iteration may include selecting a subgroup of media unit regions of interest based on a spatial relationship between the subgroup of multidimensional regions of interest.

Method 7000 may include applying a merge function on the subgroup of multidimensional regions of interest. See, for example, FIG. 1C.

Method 7000 may include applying an intersection function on the subgroup of multidimensional regions of interest. See, for example, FIG. 1C.

The merge operation of the iteration may be based on an actual size of one or more multidimensional regions of interest.

The merge operation of the iteration may be based on relationship between sizes of the multidimensional regions of interest. For example—larger multidimensional regions of interest may be maintained while smaller multidimensional regions of interest may be ignored of.

The merge operation of the iteration may be based on changes of the media unit regions of interest during at least the iteration and one or more previous iteration.

Step 7012 may be followed by step 7014 of determining identifiers that are associated with significant portions of an output of the multiple iterations.

Step 7014 may be followed by step 7016 of providing a signature that comprises the identifiers and represents the multiple objects.

Localization and Segmentation

Any of the mentioned above signature generation method provides a signature that does not explicitly includes accurate shape information. This adds to the robustness of the signature to shape related inaccuracies or to other shape related parameters.

The signature includes identifiers for identifying media regions of interest.

Each media region of interest may represent an object (for example a vehicle, a pedestrian, a road element, a human made structure, wearables, shoes, a natural element such as a tree, the sky, the sun, and the like) or a part of an object (for example—in the case of the pedestrian—a neck, a head, an arm, a leg, a thigh, a hip, a foot, an upper arm, a forearm, a wrist, and a hand). It should be noted that for object detection purposes a part of an object may be regarded as an object.

The exact shape of the object may be of interest.

FIG. 1G illustrates method 7002 of generating a hybrid representation of a media unit.

Method 7002 may include a sequence of steps 7020, 7022, 7024 and 7026.

Step 7020 may include receiving or generating the media unit.

Step 7022 may include processing the media unit by performing multiple iterations, wherein at least some of the multiple iterations comprises applying, by spanning elements of the iteration, dimension expansion process that are followed by a merge operation.

Step 7024 may include selecting, based on an output of the multiple iterations, media unit regions of interest that contributed to the output of the multiple iterations.

Step 7026 may include providing a hybrid representation, wherein the hybrid representation may include (a) shape information regarding shapes of the media unit regions of interest, and (b) a media unit signature that includes identifiers that identify the media unit regions of interest.

Step 7024 may include selecting the media regions of interest per segment out of multiple segments of the media unit. See, for example, FIG. 2.

Step 7026 may include step 7027 of generating the shape information.

The shape information may include polygons that represent shapes that substantially bound the media unit regions of interest. These polygons may be of a high degree.

In order to save storage space, the method may include step 7028 of compressing the shape information of the media unit to provide compressed shape information of the media unit.

FIG. 1H illustrates method 5002 for generating a hybrid representation of a media unit.

Method 5002 may start by step 5011 of receiving or generating a media unit.

Step 5011 may be followed by processing the media unit by performing multiple iterations, wherein at least some of the multiple iterations comprises applying, by spanning elements of the iteration, dimension expansion process that are followed by a merge operation.

The processing may be followed by steps 5060 and 5062.

The processing may include steps 5020, 5030, 5040 and 5050.

Step 5020 may include performing a k'th iteration expansion process (k may be a variable that is used to track the number of iterations).

Step 5030 may include performing a k'th iteration merge process.

Step 5040 may include changing the value of k.

Step 5050 may include checking if all required iterations were done—if so proceeding to steps 5060 and 5062. Else—jumping to step 5020.

The output of step 5020 is a k'th iteration expansion result.

The output of step 5030 is a k'th iteration merge result.

For each iteration (except the first iteration)—the merge result of the previous iteration is an input to the current iteration expansion process.

Step 5060 may include completing the generation of the signature.

Step 5062 may include generating shape information regarding shapes of media unit regions of interest. The signature and the shape information provide a hybrid representation of the media unit.

The combination of steps 5060 and 5062 amounts to a providing a hybrid representation, wherein the hybrid representation may include (a) shape information regarding shapes of the media unit regions of interest, and (b) a media unit signature that includes identifiers that identify the media unit regions of interest.

Object Detection Using Compressed Shape Information.

Object detection may include comparing a signature of an input image to signatures of one or more cluster structures in order to find one or more cluster structures that include one or more matching signatures that match the signature of the input image.

The number of input images that are compared to the cluster structures may well exceed the number of signatures of the cluster structures. For example—thousands, tens of thousands, hundreds of thousands (and even more) of input signature may be compared to much less cluster structure signatures. The ratio between the number of input images to the aggregate number of signatures of all the cluster structures may exceed ten, one hundred, one thousand, and the like.

In order to save computational resources, the shape information of the input images may be compressed.

On the other hand—the shape information of signatures that belong to the cluster structures may be uncompressed—and of higher accuracy than those of the compressed shape information.

When the higher quality is not required—the shape information of the cluster signature may also be compressed.

Compression of the shape information of cluster signatures may be based on a priority of the cluster signature, a popularity of matches to the cluster signatures, and the like.

The shape information related to an input image that matches one or more of the cluster structures may be calculated based on shape information related to matching signatures.

For example—a shape information regarding a certain identifier within the signature of the input image may be determined based on shape information related to the certain identifiers within the matching signatures.

Any operation on the shape information related to the certain identifiers within the matching signatures may be applied in order to determine the (higher accuracy) shape information of a region of interest of the input image identified by the certain identifier.

For example—the shapes may be virtually overlaid on each other and the population per pixel may define the shape.

For example—only pixels that appear in at least a majority of the overlaid shaped should be regarded as belonging to the region of interest.

Other operations may include smoothing the overlaid shapes, selecting pixels that appear in all overlaid shapes.

The compressed shape information may be ignored of or be taken into account.

FIG. 1I illustrates a matching process and a generation of a higher accuracy shape information.

It is assumed that there are multiple (M) cluster structures 4974(1)-4974(M). Each cluster structure includes cluster signatures, metadata regarding the cluster signatures, and shape information regarding the regions of interest identified by identifiers of the cluster signatures.

For example—first cluster structure 4974(1) includes multiple (N1) signatures (referred to as cluster signatures CS) CS(1,1)-CS(1,N1) 4975(1,1)-4975(1,N1), metadata 4976(1), and shape information (Shapeinfo 4977(1)) regarding shapes of regions of interest associated with identifiers of the CSs.

Yet for another example—M'th cluster structure 4974(M) includes multiple (N2) signatures (referred to as cluster signatures CS) CS(M,1)-CS(M,N2) 4975(M,1)-4975(M,N2), metadata 4976(M), and shape information (Shapeinfo 4977(M)) regarding shapes of regions of interest associated with identifiers of the CSs.

The number of signatures per concept structure may change over time—for example due to cluster reduction attempts during which a CS is removed from the structure to provide a reduced cluster structure, the reduced structure is checked to determine that the reduced cluster signature may still identify objects that were associated with the (non-reduced) cluster signature—and if so the signature may be reduced from the cluster signature.

The signatures of each cluster structures are associated to each other, wherein the association may be based on similarity of signatures and/or based on association between metadata of the signatures.

Assuming that each cluster structure is associated with a unique object—then objects of a media unit may be identified by finding cluster structures that are associated with said objects. The finding of the matching cluster structures may include comparing a signature of the media unit to signatures of the cluster structures- and searching for one or more matching signature out of the cluster signatures.

In FIG. 1I—a media unit having a hybrid representation undergoes object detection. The hybrid representation includes media unit signature 4972 and compressed shape information 4973.

The media unit signature 4972 is compared to the signatures of the M cluster structures—from CS(1,1) 4975(1,1) till CS(M,N2) 4975(M,N2).

We assume that one or more cluster structures are matching cluster structures.

Once the matching cluster structures are found the method proceeds by generating shape information that is of higher accuracy then the compressed shape information.

The generation of the shape information is done per identifier.

For each j that ranges between 1 and J (J is the number of identifiers per the media unit signature 4972) the method may perform the steps of:

Find (step 4978(j)) the shape information of the j'th identifier of each matching signature- or of each signature of the matching cluster structure.

Generate (step 4979(j)) a higher accuracy shape information of the j'th identifier.

For example—assuming that the matching signatures include CS(1,1) 2975(1,1), CS(2,5) 2975(2,5), CS(7,3) 2975(7,3) and CS(15,2) 2975(15,2), and that the j'th identifier is included in CS(1,1) 2975(1,1),CS(7,3) 2975(7,3) and CS(15,2) 2975(15,2)—then the shape information of the j'th identifier of the media unit is determined based on the shape information associated with CS(1,1) 2975(1,1),CS(7,3) 2975(7,3) and CS(15,2) 2975(15,2).

FIG. 1P illustrates an image 8000 that includes four regions of interest 8001, 8002, 8003 and 8004. The signature 8010 of image 8000 includes various identifiers including ID1 8011, ID2 8012, ID3 8013 and ID4 8014 that identify the four regions of interest 8001, 8002, 8003 and 8004.

The shapes of the four regions of interest 8001, 8002, 8003 and 8004 are four polygons. Accurate shape information regarding the shapes of these regions of interest may be generated during the generation of signature 8010.

FIG. 1J illustrates method 8030 for object detection.

Method 8030 may include the steps of method 8020 or may be preceded by steps 8022, 8024 and 8026.

Method 8030 may include a sequence of steps 8032, 8034, 8036 and 8038.

Step 8032 may include receiving or generating an input image.

Step 8034 may include generating a signature of the input image.

Step 8036 may include comparing the signature of the input image to signatures of a certain concept structure. The certain concept structure may be generated by method 8020.

Step 8038 may include determining that the input image comprises the object when at least one of the signatures of the certain concept structure matches the signature of the input image.

FIG. 2D illustrates method 8040 for object detection.

Method 8040 may include the steps of method 8020 or may be preceded by steps 8022, 8024 and 8026.

Method 8040 may include a sequence of steps 8041, 8043, 8045, 8047 and 8049.

Step 8041 may include receiving or generating an input image.

Step 8043 may include generating a signature of the input image, the signature of the input image comprises only some of the certain second image identifiers; wherein the input image of the second scale.

Step 8045 may include changing a scale of the input image to the first scale to a provide an amended input image.

Step 8047 may include generating a signature of the amended input image.

Step 8049 may include verifying that the input image comprises the object when the signature of the amended input image comprises the at least one certain first image identifier.

Object detection that is robust to angle of acquisition.

Object detection may benefit from being robust to the angle of acquisition—to the angle between the optical axis of an image sensor and a certain part of the object. This allows the detection process to be more reliable, use fewer different clusters (may not require multiple clusters for identifying the same object from different images).

FIG. 1K illustrates method 8120 that includes the following steps:

Step 8122 of receiving or generating images of objects taken from different angles.

Step 8124 of finding images of objects taken from different angles that are close to each other. Close enough may be less than 1, 5, 10, 15 and 20 degrees—but the closeness may be better reflected by the reception of substantially the same signature.

Step 8126 of linking between the images of similar signatures. This may include searching for local similarities. The similarities are local in the sense that they are calculated per a subset of signatures. For example—assuming that the similarity is determined per two images—then a first signature may be linked to a second signature that is similar to the first image. A third signature may be linked to the second image based on the similarity between the second and third signatures—and even regardless of the relationship between the first and third signatures.

Step 8126 may include generating a concept data structure that includes the similar signatures.

This so-called local or sliding window approach, in addition to the acquisition of enough images (that will statistically provide a large angular coverage) will enable to generate a concept structure that include signatures of an object taken at multiple directions.

Signature Tailored Matching Threshold.

Object detection may be implemented by (a) receiving or generating concept structures that include signatures of media units and related metadata, (b) receiving a new media unit, generating a new media unit signature, and (c) comparing the new media unit signature to the concept signatures of the concept structures.

The comparison may include comparing new media unit signature identifiers (identifiers of objects that appear in the new media unit) to concept signature identifiers and determining, based on a signature matching criteria whether the new media unit signature matches a concept signature. If such a match is found then the new media unit is regarded as including the object associated with that concept structure.

It was found that by applying an adjustable signature matching criteria, the matching process may be highly effective and may adapt itself to the statistics of appearance of identifiers in different situations. For example—a match may be obtained when a relatively rear but highly distinguishing identifier appears in the new media unit signature and in a cluster signature, but a mismatch may be declared when multiple common and slightly distinguishing identifiers appear in the new media unit signature and in a cluster signature.

FIG. 1L illustrates method 8200 for object detection.

Method 8200 may include:

Step 8210 of receiving an input image.

Step 8212 of generating a signature of the input image.

Step 8214 of comparing the signature of the input image to signatures of a concept structure.

Step 8216 of determining whether the signature of the input image matches any of the signatures of the concept structure based on signature matching criteria, wherein each signature of the concept structure is associated within a signature matching criterion that is determined based on an object detection parameter of the signature.

Step 8218 of concluding that the input image comprises an object associated with the concept structure based on an outcome of the determining.

The signature matching criteria may be a minimal number of matching identifiers that indicate of a match. For example—assuming a signature that include few tens of identifiers, the minimal number may vary between a single identifier to all of the identifiers of the signature.

It should be noted that an input image may include multiple objects and that a signature of the input image may match multiple cluster structures. Method 8200 is applicable to all of the matching processes- and that the signature matching criteria may be set for each signature of each cluster structure.

Step 8210 may be preceded by step 8202 of determining each signature matching criterion by evaluating object detection capabilities of the signature under different signature matching criteria.

Step 8202 may include:

Step 8203 of receiving or generating signatures of a group of test images.

Step 8204 of calculating the object detection capability of the signature, for each signature matching criterion of the different signature matching criteria.

Step 8206 of selecting the signature matching criterion based on the object detection capabilities of the signature under the different signature matching criteria.

The object detection capability may reflect a percent of signatures of the group of test images that match the signature.

The selecting of the signature matching criterion comprises selecting the signature matching criterion that once applied results in a percent of signatures of the group of test images that match the signature that is closets to a predefined desired percent of signatures of the group of test images that match the signature.

The object detection capability may reflect a significant change in the percent of signatures of the group of test images that match the signature. For example—assuming, that the signature matching criteria is a minimal number of matching identifiers and that changing the value of the minimal numbers may change the percentage of matching test images. A substantial change in the percentage (for example a change of more than 10, 20, 30, 40 percent) may be indicative of the desired value. The desired value may be set before the substantial change, proximate to the substantial change, and the like.

For example, referring to FIG. 1I, cluster signatures CS(1,1), CS(2,5), CS(7,3) and CS(15,2) match unit signature 4972. Each of these matches may apply a unique signature matching criterion.

Examples of Systems

FIG. 1M illustrates an example of a system capable of executing one or more of the mentioned above methods.

The system include various components, elements and/or units.

A component element and/or unit may be a processing circuitry may be implemented as a central processing unit (CPU), and/or one or more other integrated circuits such as application-specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), full-custom integrated circuits, etc., or a combination of such integrated circuits.

Alternatively, each component element and/or unit may implemented in hardware, firmware, or software that may be executed by a processing circuitry.

System 4900 may include sensing unit 4902, communication unit 4904, input 4911, one or more processors—such as processor 4950, and output 4919. The communication unit 4904 may include the input and/or the output. The communication unit 4904 may communicate with any entity—within the vehicle (for example driver device, passenger device, multimedia device), outside the vehicle (another vehicle, another computerized system—such as out-of-vehicle computerized system 4820 of FIG. 1N, another road user, another human outside the vehicle), and the like.

Input and/or output may be any suitable communications component such as a network interface card, universal serial bus (USB) port, disk reader, modem or transceiver that may be operative to use protocols such as are known in the art to communicate either directly, or indirectly, with other elements of the system.

Processor 4950 may include at least some out of (and thus may not include at least one out of):

-   -   Multiple spanning elements 4951(q).     -   Multiple merge elements 4952(r).     -   Object detector 4953.     -   Cluster manager 4954.     -   Controller 4955.     -   Selection unit 4956.     -   Object detection determination unit 4957.     -   Signature generator 4958.     -   Movement information unit 4959.     -   Identifier unit 4960.

While system 4900 includes a sensing unit 4902—is should be noted that it may receive sensed information from other sensors and/or that the sensing unit does not belong to the system. The system may receive information from one or more sensors located in the vehicle, associated with the vehicle, and/or located outside the vehicle.

Any method illustrated in the specification may be fully or partially executed by system 4900, and/or may be fully or partially executed by one or more other computerized system, and/or by one or more computerized systems—for example by task allocations between computerized systems, by a cooperation (for example—exchange of information, exchange of decisions, any allocation of resources, collaborative decision, and the like) between multiple computerized systems.

The one or more other computerized systems may be, for example, out-of-vehicle computerized system 4820 of FIG. 1N, any other out-of-vehicle computerized system, one or more other in-vehicle systems, a computerized device of a person within the vehicle, any computerized system outside the vehicle—including for example a computerized system of another vehicle.

An example of an other in-vehicle system is denoted 4830 in FIGS. 1N and 1 s located within vehicle 4800 that drives along road 4820.

System 4900 may obtain sensed information from any type of sensors—a camera, one or more sensors implemented using any suitable imaging technology instead of, or in addition to, a conventional camera, an infrared sensor, a radar, an ultrasound sensor, any electro-optic sensor, a radiography sensor, a LIDAR (light detection and ranging), telemetry ECU sensor, shock sensor, etc.

System 4900 and/or other in-vehicle system is denoted 4830 may use supervised and/or unsupervised learning to perform any method executed by them.

The other in-vehicle system 4830 may be an autonomous driving system, an advance driver assistance system, or may differ from an autonomous driving system and from an advance driver assistance system.

The other in-vehicle system 4830 may include processing circuitry 210, input/output (I/O) module 220, one or more sensors 233, and database 270. The processing circuitry 210 may execute any task is it assigned or programmed to perform in relation to any of the methods illustrate din the application. Alternatively—the other in-vehicle system 4830 may include another module for executing (alone or with the processing circuit) any such task. For example—the processing circuitry may execute instructions to provide an autonomous driving manager functionality. Alternatively—another circuit or module of the in-vehicle system 4830 may provide the autonomous driving manager functionality.

FIG. 1O illustrates method 7002 of generating a hybrid representation of a media unit.

Method 7002 may include a sequence of steps 7020, 7022, 7024 and 7026.

Step 7020 may include receiving or generating the media unit.

Step 7022 may include processing the media unit by performing multiple iterations, wherein at least some of the multiple iterations comprises applying, by spanning elements of the iteration, dimension expansion process that are followed by a merge operation.

Step 7024 may include selecting, based on an output of the multiple iterations, media unit regions of interest that contributed to the output of the multiple iterations.

Step 7026 may include providing a hybrid representation, wherein the hybrid representation may include (a) shape information regarding shapes of the media unit regions of interest, and (b) a media unit signature that includes identifiers that identify the media unit regions of interest.

Step 7024 may include selecting the media regions of interest per segment out of multiple segments of the media unit. See, for example, FIG. 2.

Step 7026 may include step 7027 of generating the shape information.

The shape information may include polygons that represent shapes that substantially bound the media unit regions of interest. These polygons may be of a high degree.

In order to save storage space, the method may include step 7028 of compressing the shape information of the media unit to provide compressed shape information of the media unit.

FIG. 1P illustrates method 8020 for scale invariant object detection.

Method 8020 may include a first sequence of steps that may include step 8022, 8024, 8026 and 8028.

Step 8022 may include receiving or generating a first image in which an object appears in a first scale and a second image in which the object appears in a second scale that differs from the first scale.

Step 8024 may include generating a first image signature and a second image signature.

The first image signature includes a first group of at least one certain first image identifier that identifies at least a part of the object.

The second image signature includes a second group of certain second image identifiers that identify different parts of the object.

The second group is larger than first group—as the second group has more members than the first group.

Step 8026 may include linking between the at least one certain first image identifier and the certain second image identifiers.

Step 8026 may include linking between the first image signature, the second image signature and the object.

Step 8026 may include adding the first signature and the second signature to a certain concept structure that is associated with the object.

Step 8028 may include determining whether an input image includes the object based, at least in part, on the linking. The input image differs from the first and second images.

The determining may include determining that the input image includes the object when a signature of the input image includes the at least one certain first image identifier or the certain second image identifiers.

The determining may include determining that the input image includes the object when the signature of the input image includes only a part of the at least one certain first image identifier or only a part of the certain second image identifiers.

The linking may be performed for more than two images in which the object appears in more than two scales.

FIG. 1Q illustrates method 8200 for object detection.

Method 8200 may include:

Step 8210 of receiving an input image.

Step 8212 of generating a signature of the input image.

Step 8214 of comparing the signature of the input image to signatures of a concept structure.

Step 8216 of determining whether the signature of the input image matches any of the signatures of the concept structure based on signature matching criteria, wherein each signature of the concept structure is associated within a signature matching criterion that is determined based on an object detection parameter of the signature.

Step 8218 of concluding that the input image comprises an object associated with the concept structure based on an outcome of the determining.

The signature matching criteria may be a minimal number of matching identifiers that indicate of a match. For example—assuming a signature that include few tens of identifiers, the minimal number may vary between a single identifier to all of the identifiers of the signature.

It should be noted that an input image may include multiple objects and that a signature of the input image may match multiple cluster structures. Method 8200 is applicable to all of the matching processes- and that the signature matching criteria may be set for each signature of each cluster structure.

Step 8210 may be preceded by step 8202 of determining each signature matching criterion by evaluating object detection capabilities of the signature under different signature matching criteria.

Step 8202 may include:

Step 8203 of receiving or generating signatures of a group of test images.

Step 8204 of calculating the object detection capability of the signature, for each signature matching criterion of the different signature matching criteria.

Step 8206 of selecting the signature matching criterion based on the object detection capabilities of the signature under the different signature matching criteria.

The object detection capability may reflect a percent of signatures of the group of test images that match the signature.

The selecting of the signature matching criterion comprises selecting the signature matching criterion that once applied results in a percent of signatures of the group of test images that match the signature that is closets to a predefined desired percent of signatures of the group of test images that match the signature.

The object detection capability may reflect a significant change in the percent of signatures of the group of test images that match the signature. For example—assuming, that the signature matching criteria is a minimal number of matching identifiers and that changing the value of the minimal numbers may change the percentage of matching test images. A substantial change in the percentage (for example a change of more than 10, 20, 30, 40 percent) may be indicative of the desired value. The desired value may be set before the substantial change, proximate to the substantial change, and the like.

Any reference in the specification to a method should be applied mutatis mutandis to a system capable of executing the method and should be applied mutatis mutandis to a non-transitory computer readable medium that stores instructions that once executed by a computer result in the execution of the method.

Any reference in the specification to a system and any other component should be applied mutatis mutandis to a method that may be executed by a system and should be applied mutatis mutandis to a non-transitory computer readable medium that stores instructions that may be executed by the system.

Any reference in the specification to a non-transitory computer readable medium should be applied mutatis mutandis to a system capable of executing the instructions stored in the non-transitory computer readable medium and should be applied mutatis mutandis to method that may be executed by a computer that reads the instructions stored in the non-transitory computer readable medium.

Any combination of any module or unit listed in any of the figures, any part of the specification and/or any claims may be provided. Especially any combination of any claimed feature may be provided.

Any reference to the term “comprising” or “having” should be interpreted also as referring to “consisting” of “essentially consisting of”. For example—a method that comprises certain steps can include additional steps, can be limited to the certain steps or may include additional steps that do not materially affect the basic and novel characteristics of the method—respectively.

The invention may also be implemented in a computer program for running on a computer system, at least including code portions for performing steps of a method according to the invention when run on a programmable apparatus, such as a computer system or enabling a programmable apparatus to perform functions of a device or system according to the invention. The computer program may cause the storage system to allocate disk drives to disk drive groups.

A computer program is a list of instructions such as a particular application program and/or an operating system. The computer program may for instance include one or more of: a subroutine, a function, a procedure, an object method, an object implementation, an executable application, an applet, a servlet, a source code, an object code, a shared library/dynamic load library and/or other sequence of instructions designed for execution on a computer system.

The computer program may be stored internally on a computer program product such as non-transitory computer readable medium. All or some of the computer program may be provided on non-transitory computer readable media permanently, removably or remotely coupled to an information processing system. The non-transitory computer readable media may include, for example and without limitation, any number of the following: magnetic storage media including disk and tape storage media; optical storage media such as compact disk media (e.g., CD-ROM, CD-R, etc.) and digital video disk storage media; nonvolatile memory storage media including semiconductor-based memory units such as FLASH memory, EEPROM, EPROM, ROM; ferromagnetic digital memories; MRAM; volatile storage media including registers, buffers or caches, main memory, RAM, etc. A computer process typically includes an executing (running) program or portion of a program, current program values and state information, and the resources used by the operating system to manage the execution of the process. An operating system (OS) is the software that manages the sharing of the resources of a computer and provides programmers with an interface used to access those resources. An operating system processes system data and user input, and responds by allocating and managing tasks and internal system resources as a service to users and programs of the system. The computer system may for instance include at least one processing unit, associated memory and a number of input/output (I/O) devices. When executing the computer program, the computer system processes information according to the computer program and produces resultant output information via I/O devices.

In the foregoing specification, the invention has been described with reference to specific examples of embodiments of the invention. It will, however, be evident that various modifications and changes may be made therein without departing from the broader spirit and scope of the invention as set forth in the appended claims.

Moreover, the terms “front,” “back,” “top,” “bottom,” “over,” “under” and the like in the description and in the claims, if any, are used for descriptive purposes and not necessarily for describing permanent relative positions. It is understood that the terms so used are interchangeable under appropriate circumstances such that the embodiments of the invention described herein are, for example, capable of operation in other orientations than those illustrated or otherwise described herein.

Those skilled in the art will recognize that the boundaries between logic blocks are merely illustrative and that alternative embodiments may merge logic blocks or circuit elements or impose an alternate decomposition of functionality upon various logic blocks or circuit elements. Thus, it is to be understood that the architectures depicted herein are merely exemplary, and that in fact many other architectures may be implemented which achieve the same functionality.

Any arrangement of components to achieve the same functionality is effectively “associated” such that the desired functionality is achieved. Hence, any two components herein combined to achieve a particular functionality may be seen as “associated with” each other such that the desired functionality is achieved, irrespective of architectures or intermedial components. Likewise, any two components so associated can also be viewed as being “operably connected,” or “operably coupled,” to each other to achieve the desired functionality.

Furthermore, those skilled in the art will recognize that boundaries between the above described operations merely illustrative. The multiple operations may be combined into a single operation, a single operation may be distributed in additional operations and operations may be executed at least partially overlapping in time. Moreover, alternative embodiments may include multiple instances of a particular operation, and the order of operations may be altered in various other embodiments. Also for example, in one embodiment, the illustrated examples may be implemented as circuitry located on a single integrated circuit or within a same device. Alternatively, the examples may be implemented as any number of separate integrated circuits or separate devices interconnected with each other in a suitable manner.

Also for example, the examples, or portions thereof, may implemented as soft or code representations of physical circuitry or of logical representations convertible into physical circuitry, such as in a hardware description language of any appropriate type.

Also, the invention is not limited to physical devices or units implemented in non-programmable hardware but can also be applied in programmable devices or units able to perform the desired device functions by operating in accordance with suitable program code, such as mainframes, minicomputers, servers, workstations, personal computers, notepads, personal digital assistants, electronic games, automotive and other embedded systems, cell phones and various other wireless devices, commonly denoted in this application as ‘computer systems’.

However, other modifications, variations and alternatives are also possible. The specifications and drawings are, accordingly, to be regarded in an illustrative rather than in a restrictive sense.

In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word ‘comprising’ does not exclude the presence of other elements or steps then those listed in a claim. Furthermore, the terms “a” or “an,” as used herein, are defined as one or more than one. Also, the use of introductory phrases such as “at least one” and “one or more” in the claims should not be construed to imply that the introduction of another claim element by the indefinite articles “a” or “an” limits any particular claim containing such introduced claim element to inventions containing only one such element, even when the same claim includes the introductory phrases “one or more” or “at least one” and indefinite articles such as “a” or “an.” The same holds true for the use of definite articles. Unless stated otherwise, terms such as “first” and “second” are used to arbitrarily distinguish between the elements such terms describe. Thus, these terms are not necessarily intended to indicate temporal or other prioritization of such elements. The mere fact that certain measures are recited in mutually different claims does not indicate that a combination of these measures cannot be used to advantage.

While certain features of the invention have been illustrated and described herein, many modifications, substitutions, changes, and equivalents will now occur to those of ordinary skill in the art. It is, therefore, to be understood that the appended claims are intended to cover all such modifications and changes as fall within the true spirit of the invention. 

What is claimed is:
 1. A method for electrical scooter alert, the method comprises: sensing sensed information about an environment of a vehicle; detecting, based on the sensed information, a situation related to the environment; detecting, based on the sensed information, a electrical scooter within the environment; predicting, using a machine leaning process and based in the situation, a future behavior of the electrical scooter and an impact of the future behavior of the electrical scooter on a future progress of the vehicle; and responding to the predicting.
 2. The method according to claim 1 wherein the responding to the predicting is responsive to a probability distribution of potential future behaviors of the electrical scooter.
 3. The method according to claim 1 wherein the responding is responsive to an existence of at least one potential relevant future behavior of the electrical scooter, wherein each of the at least potential relevant future behavior of the electrical scooter (a) has probability of occurrence that exceeds a threshold, and (b) impacts the future progress of the vehicle when occurs.
 4. The method according to claim 1 wherein the responding is selected out of generating a human perceivable information about the predicting, requesting an autonomous module to drive the vehicle based on the predicting; proposing a route of the vehicle; storing an outcome of the predicting; activating a driving mode in response to the outcome of the predicting; activating safety measures in response to the outcome of the predicting; and generating a human perceivable alert outside the vehicle.
 5. The method according to claim 1 wherein the machine learning is trained by a training process that comprises: generating electrical scooter identifiers based on a first set of tagged sensed information units and a second set of untagged sensed information units; monitoring behaviors of electrical scooters at multiple situations to provide monitoring results, wherein the monitoring comprises detecting one or more electrical scooter identifiers within sensed information units obtained during one or more monitoring periods; generating, based on the monitoring results, situation behavioral information indicative of behaviors of electrical scooters at different situations.
 6. The method according to claim 5 comprising classifying the electrical scooters to classes; and wherein the training process comprises generating situation behavioral information indicative of behaviors of electrical scooters of different classes at different situations.
 7. The method according to claim 5 wherein the generating of the electrical scooter identifiers comprises generating signatures of the electrical scooters.
 8. The method according to claim 7 comprising clustering the signatures of electrical scooters to generate electrical scooter classes.
 9. A non-transitory computer readable medium for electrical scooter alert, the non-transitory computer readable medium comprises: sensing sensed information about an environment of a vehicle; detecting, based on the sensed information, a situation related to the environment; detecting, based on the sensed information, a electrical scooter within the environment; predicting, using a machine leaning process and based in the situation, a future behavior of the electrical scooter and an impact of the future behavior of the electrical scooter on a future progress of the vehicle; and responding to the predicting.
 10. The non-transitory computer readable medium according to claim 9 wherein the responding to the predicting is responsive to a probability distribution of potential future behaviors of the electrical scooter.
 11. The non-transitory computer readable medium according to claim 9 wherein the responding is responsive to an existence of at least one potential relevant future behavior of the electrical scooter, wherein each of the at least potential relevant future behavior of the electrical scooter (a) has probability of occurrence that exceeds a threshold, and (b) impacts the future progress of the vehicle when occurs.
 12. The non-transitory computer readable medium according to claim 9 wherein the responding is selected out of generating a human perceivable information about the predicting, requesting an autonomous module to drive the vehicle based on the predicting; proposing a route of the vehicle; storing an outcome of the predicting; activating a driving mode in response to the outcome of the predicting; activating safety measures in response to the outcome of the predicting; and generating a human perceivable alert outside the vehicle.
 13. The non-transitory computer readable medium according to claim 9 wherein the machine learning is trained by a training process that comprises: generating electrical scooter identifiers based on a first set of tagged sensed information units and a second set of untagged sensed information units; monitoring behaviors of electrical scooters at multiple situations to provide monitoring results, wherein the monitoring comprises detecting one or more electrical scooter identifiers within sensed information units obtained during one or more monitoring periods; generating, based on the monitoring results, situation behavioral information indicative of behaviors of electrical scooters at different situations.
 14. The non-transitory computer readable medium according to claim 13 that stores instructions for classifying the electrical scooters to classes; and wherein the training process comprises generating situation behavioral information indicative of behaviors of electrical scooters of different classes at different situations.
 15. The non-transitory computer readable medium according to claim 13 wherein the generating of the electrical scooter identifiers comprises generating signatures of the electrical scooters.
 16. The non-transitory computer readable medium according to claim 15 that stores instructions for clustering the signatures of electrical scooters to generate electrical scooter classes. 